Sensitivity (also called recall or true positive rate) is the proportion of actual positives correctly identified: TP / (TP + FN). A highly sensitive test misses few true cases.

Specificity (true negative rate) is the proportion of actual negatives correctly identified: TN / (TN + FP). A highly specific test rarely flags healthy people as positive.

Positive Predictive Value (PPV) is the probability that a positive test result reflects a true positive: TP / (TP + FP). It rises with prevalence.

Negative Predictive Value (NPV) is the probability that a negative test result reflects a true negative: TN / (TN + FN). It falls as prevalence increases.

What are likelihood ratios?

LR+ = sensitivity / (1 − specificity). LR− = (1 − sensitivity) / specificity. Values far from 1 indicate a more useful test. LR+ > 10 or LR− < 0.1 are considered strong.

What is the F1 score?

F1 is the harmonic mean of precision (PPV) and recall (sensitivity): 2 × PPV × Sensitivity / (PPV + Sensitivity). Commonly used in machine learning to balance both metrics.

Sensitivity & Specificity Calculator

Name: Sensitivity & Specificity Calculator
Availability: InStock
Author: Nham Vu

Enter TP, FP, FN, TN from a 2x2 confusion matrix to get sensitivity, specificity, PPV, NPV, accuracy, LR+, LR−, and F1 score.

2×2 Confusion Matrix

Enter counts for each cell. All values must be non-negative integers.

Condition
Positive

Condition
Negative

Test
Positive

TP True Positive

FP False Positive

Test
Negative

FN False Negative

TN True Negative

Sensitivity —

TP / (TP + FN) — True Positive Rate / Recall

Specificity —

TN / (TN + FP) — True Negative Rate

PPV (Precision) —

TP / (TP + FP) — Positive Predictive Value

NPV —

TN / (TN + FN) — Negative Predictive Value

LR+ (Positive LR) —

Sensitivity / (1 − Specificity). Values >10 = strong test.

LR− (Negative LR) —

(1 − Sensitivity) / Specificity. Values <0.1 = strong test.

FPR (Fall-out) —

FP / (FP + TN) — False Positive Rate = 1 − Specificity

FNR (Miss Rate) —

FN / (FN + TP) — False Negative Rate = 1 − Sensitivity

Fill in the matrix cells and click Calculate to see all metrics.

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Summary

Enter TP, FP, FN, TN from a 2x2 confusion matrix to get sensitivity, specificity, PPV, NPV, accuracy, LR+, LR−, and F1 score.

How it works

Enter the four cells of your 2x2 confusion matrix: TP, FP, FN, TN.
The calculator instantly computes all diagnostic performance metrics.
Sensitivity (recall) = TP / (TP + FN) — ability to detect true positives.
Specificity = TN / (TN + FP) — ability to rule out true negatives.
PPV and NPV depend on both test performance and disease prevalence in your sample.
Likelihood ratios (LR+ and LR−) summarize the test's discriminating power independent of prevalence.

Use cases

Evaluate a clinical screening test after a pilot study.
Compare two diagnostic tests using their confusion matrices.
Calculate F1 score for a binary machine-learning classifier.
Determine whether a lab assay meets regulatory sensitivity thresholds.
Assess COVID-19 or infectious disease rapid test performance.
Validate a medical device classification algorithm.

Frequently Asked Questions

Last updated: 2026-06-11 · Reviewed by Nham Vu

Sensitivity & Specificity Calculator

2×2 Confusion Matrix

Summary

How it works

Use cases

Frequently Asked Questions

What is sensitivity?

What is specificity?

What is PPV?

What is NPV?

What are likelihood ratios?

What is the F1 score?